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Fast Training of Deep LSTM Networks

  • Wen YuEmail author
  • Xiaoou Li
  • Jesus Gonzalez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Abstract

Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks. However, the LSTM training method, such as backward propagation through time (BPTT), is really slow.

In this paper, by separating the LSTM cell into forward and recurrent substructures, we propose a much simpler and faster training method than the BPTT. The deep LSTM is modified by combining the deep RNN with the multilayer perceptron (MLP). The simulation results show that our fast training method for LSTM is better than BPTT for LSTM.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Control AutomáticoCINVESTAV-IPN (National Polytechnic Institute)Mexico CityMexico
  2. 2.Departamento de ComputaciónCINVESTAV-IPN (National Polytechnic Institute)Mexico CityMexico

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